Data-Mining-Techniques-A-Tool-For-Knowledge-Management-System-In-Agriculture.pdf

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Data mining techniques: A tool for knowledge management system in
agriculture
Article in International Journal of Scientific & Technology Research · January 2012
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INTERNATIONAL JOURNAL OF SCIENTIFIC & TECHNOLOGY RESEARCH VOLUME 1, ISSUE 5, JUNE 2012 ISSN 2277-8616
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Data Mining Techniques: A Tool For
Knowledge Management System In
Agriculture
Latika Sharma, Nitu Mehta
Abstract: Agriculture data is highly diversified in terms of nature, interdependencies and resources. For balanced and sustainable development of
agriculture these resources need to be evaluated, monitored and analyzed so that proper policy implication could be drawn. Recently knowledge
management in agriculture facilitating extraction, storage, retrieval, transformation, dissemination and utilization of knowledge in agriculture is
underway. Data mining techniques till now used extensively in business and corporate sectors may be used in agriculture for data characterization,
discrimination and predictive and forecasting purposes. Some use of data mining in soil characteristic evaluation has already been attempted. This
paper attempts to bring out the characteristic computational needs of agriculture data which is essentially seasonal and uncertain along with some
suggestion regarding the use of data mining techniques as a tool for knowledge management in agriculture.
Keywords: Data Mining, Knowledge Management System, Data Warehouses ,KDD, Agriculture System, and OLAP.
Agriculture in India
The Indian Agriculture is highly diversified in terms of its
climate, soil, crops, horticultural crops, plantation crops,
livestock resources, fisheries resources, water resources,
etc. the diversity of its agricultural sector is both a bane
and boon to the social, economic, and cultural bases of
India’s vast population. Moreover, the diversity among
resources generates interactions among many different
macro and micro factors, and is further complicated with
the interdependencies that exist among these. These
resources need to be evaluated, monitored, and allocated
optimally for balanced and sustainable development of the
country.
Knowledge Management System in Agriculture
Knowledge Management System is a platform facilitating
extraction, storage, retrieval, integration, transformation,
visualization, analysis, dissemination, and utilization of
knowledge. It is a process consisting of identifying valid
and potentially useful data,
Latika Sharma is currently working as Assist.
Professor in the Dept. of Agriculture Economics &
Management, RCA, MPUAT, Udaipur.
Email-latika2@gmail.com
Nitu Mehta is currently working as Senior
Research Fellow in the NAIP Project, Dept. of
Agriculture Economics & Management, RCA,
MPUAT, Udaipur.
Email-nitumehta82@gmail.com
Establishment of databases and data warehouse;
knowledge discovery from database/data warehouse
(KDD) and development of the mechanism of
dissemination of knowledge on information networks as
per requirements of user groups. Since there is a large
number of data collection agencies and equally diverse
resources for which the information is collected, it is easy
to visualize the heterogeneity of information from the
agricultural sector. As stated earlier, the problem is
compounded by the fact that there are no common
standards that are applied in data collection. Designing
data warehouses to integrate the collected information
poses a formidable challenge to any data warehouses
architect. In order to use the information for planning and
decision-making level, data have to be integrated and
aggregated properly. The challenges that may be faced in
Knowledge Management System in agriculture are:
a) Knowledge evaluation- involves assessing the
worth of information.
b) Knowledge processing- Involves the identification
of techniques to acquire, store, process and
distribute information and sometimes it is
necessary to document how certain decisions
were reached.
c) Knowledge Implementation- (i) commitment to
change, learn and innovate by organization (ii)
extraction of meaning from information that may
have an impact on specific mission and (iii)
lessons learned from feedback can be stored for
future to help others facing the similar problem(s).

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Data Set 1
Data Set 1
Data Set 1
Knowledge
extractor
(e.g. data
mining)
Knowledge Server
1
Knowledge Server
n
Knowledge User 1
Knowledge User 2
Knowledge User 1
Knowledge Management System (KMS)
Figure.1. Knowledge management System Frame Work
Figure.1. Knowledge management System Frame Work
Data
Data
Rules
Knowledge Management System Frame Work
Following Xu and Zhang (2004), a KMS can be visualized
as a system [ Fig. 1] in which data and rules enter into the
system as inputs, knowledge extraction is undertaken
based on the input rules, the extracted knowledge is
managed and knowledge based services are provided to
the stake holders.
A Knowledge Management System can be split into sub
components:
1. Repositories- These hold explicated formal &
informal knowledge and the rules associated with
them for collection, refining, managing, validating,
maintaining, interpretating and distributing content.
2. Collaborative platforms- These support distributed
work and incorporate pointers, skills databases, expert
locators and informal communication channels.
3. Networks- networks support communications and
conversion. They include broad bands, leased lines,
intranets, extranets et.
4. Culture- Culture enablers that encourage sharing and
use.
Data Warehouses and its Application in Agriculture
The World Wide Web is producing voluminous data,
differentiated for type and available to varied users.
Furthermore, the continuing progress in ICT over the past
two decades has
led to the availability of powerful computers, data
collection equipments, and storage media allowing
transaction management, information retrieval, and
data analysis over massive amount of heterogeneous
data. The availability of data over the Internet now is
in different formats: structured (e.g. plain text,
audio/video) data. Thus, new data management
systems, able to take advantage of these
heterogeneous data, are emerging and will play a vital
role in the information industry. Thus, heterogeneous
database systems emerge and play a vital role in the
information industry. A data warehouse is a repository
of integrated information available for queries and
analysis. Data and information are extracted from
heterogeneous sources as this are generated. Data
warehouse is a data base that is used to hold data for
reporting and analysis.

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On Line Analytical Processing (OLAP)
OLAP is an approach to swiftly answer multi-dimensional
analytical queries. Data mining is a part of OLAP with
application such as forecasting or prediction in agriculture.
It provides an opportunity of viewing agriculture data from
different points of view to better understand what that data
means OLAP has been used extensively for analysis of
Soil physical characteristics. The recent advances in data
base technology and data warehouses, the multi
dimensional data base, OLAP, SOLAP (Spatial OLAP) and
data mining technologies are being successfully applied to
the management of national resources.
Computational Needs of Agriculture data
Agriculture data is often associated with uncertainty
because of measurement inaccuracy, sampling
discrepancy, outdated data sources, or other errors. In
recent years, there has been much research on the
management of uncertain data in databases, such as the
representation of uncertainty in database and querying
data with uncertainty. However, little research work has
addressed the issue of mining uncertain data. We note
that with uncertainty, data values are no longer atomic. To
apply traditional data mining techniques, uncertain data
has to be summarized into atomic values. Discrepancy in
the summarized recorded values and the actual values
could seriously affect the quality of the mining results.
What kind of Data can be mined in Agriculture?
In principle, data mining is not specific to one type of
media or data. Data mining should be applicable to any
kind of information repository. However, algorithms and
approaches may differ when applied to different types of
The most commonly used query language for relational
database is SQL, which allows retrieval and manipulation
of the data stored in the tables, as well as the calculation
of aggregate functions such as average, sum, min, max
and count. For instance, an SQL query to select the
farmers grouped by category (Land Holding group) would
be: data. Indeed, the challenges presented by different
types use and studied for databases, including relational
databases, object-relational databases and object oriented
databases, data warehouses, transactional databases,
unstructured and semi structured repositories such as the
of data vary significantly. Data mining is being put into
World Wide Web, advanced databases such as spatial
databases, multimedia databases, time-series databases
and textual databases, and even flat files. Here are some
examples in more detail:
Flat files: Flat files are actually the most common data
source for data mining algorithms, especially at the
research level. Flat files are simple data files in text or
binary format with a structure known by the data mining
algorithm to be applied. The data in these files can be
transactions, time-series data, scientific measurements,
etc.
Relational Databases: Briefly, a relational database
consists of a set of tables containing either values of entity
attributes, or values of attributes from entity relationships.
Tables have columns and rows, where columns represent
attributes and rows represent tuples. A tuple in a relational
table corresponds to either an object or a relationship
between objects and is identified by a set of attribute
values representing a unique key. In Figure 2 we present
some relations farmers and subsides in a fictitious
government support program of subsidies for small
farmers.
Figure 2: Fragments of some relations from a relational database for Agriculture AgAgriculture

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The most commonly used query language for relational
database is SQL, which allows retrieval and manipulation
of the data stored in the tables, as well as the calculation
of aggregate functions such as average, sum, min, max
and count. For instance, an SQL query to select the
farmers grouped by category (Land Holding group) would
be:
SELECT count (*) FROM Subsidies WHERE type=small
farmer GROUP BY category.
Transaction Databases: A transaction database is a set
of records representing transactions, each with a time
stamp, an identifier and a set of items. Associated with the
transaction files could also be descriptive data for the
items. For example, in the case of the video store, the
rentals table such as shown in Figure 1.5 represents the
Data mining algorithms using relational databases can be
more versatile than data mining algorithms specifically
written for flat files, since they can take advantage of the
SQL could provide, such as predicting, comparing,
detecting deviations, etc.
Data Warehouses: A data warehouse as a storehouse is
a repository of data collected from multiple data sources
(often heterogeneous) and is intended to be used as a
whole under the same unified schema. A data warehouse
gives the option to analyze data from different sources
under the same roof.
transaction database. Each record is a rental contract with
a customer identifier, a date, and the list of items rented
(i.e. video tapes, games, VCR, etc.). Since relational
databases do not allow nested tables (i.e. a set as
attribute value), transactions are usually stored in flat files
or stored in two normalized transaction tables, one for the
transactions and one for the transaction items. One typical
Figure 3: A multi-dimensional data cube structure commonly used in data for data warehousing
Sum
By time & village
By Category
Village A
Village B
Village C Small
Marginal
Land less
By Village
Aggregate
Small
Marginal
Land less
Sum
Group by Category
(Land holding size)
Sum
By Time
Land less
Marginal
Small
Cross Tab By Category (land
holding size)
Q1 Q3 Q4
Q2
Sum

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data mining analysis on such data is the so-called market
basket analysis or association rules in which associations
Spatial Databases: Spatial databases are databases that,
in addition to usual data, store geographical information
like
Time-Series Databases: Time-series databases contain
time related data such stock market data or logged
activities. These databases usually have a continuous flow
of new data coming in, which sometimes causes the need
for a
between items occurring together or in sequence are
studied.
maps, and global or regional positioning. Such spatial
databases present new challenges to data mining
algorithms.
challenging real time analysis. Data mining in such
databases commonly includes the study of trends and
correlations between evolutions of different variables, as
well as the prediction of trends and movements of the
variables in time. Figure 6 shows some examples of time-
series data.
Figure 4: Fragment of a transaction database for the credit transaction of a farmer through
farmer credit card scheme.
Figure 5 : Visualization of Spatial OLAP (from Geo Miner system)

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Actual
Predicted
Forecast
Actual
Predicted
Forecast
150
100
50
0
7500
6500
5500
4500
3500
2500
1500
Price/RS
Time
MSD:
MAD:
MAPE:
Alpha:
Smoothing Constant
131435
228
8
1.042
Single Exponential Smoothing
What can be discovered?
1) Data Characterization: Data characterization is a
summarization of general features of objects in a
target class, and produces what is called
characteristic rules. The data relevant to a user-
specified class are normally retrieved by a database
query and run through a summarization module to
extract the essence of the data at different levels of
abstractions. For example, one may want to
characterize the farmers according to their family
income or household size or fixed assets. We may
wish to know if there is a pattern in the cropping
pattern or consumption pattern of farmers according
to land holding size group. With a data cube
containing summarization of data, simple OLAP
operations fit the purpose of data characterization.
2) Data Discrimination :Data discrimination produces
what are called discriminant rules and is basically
the comparison of the general features of objects
between two classes referred to as the target
class and the contrasting class. For example, one
may want to compare the living Standards and
family income of farmers availing subsidy support by
the government with those of not getting their
support. The techniques used for data
discrimination are very similar to the techniques
used for data characterization with the exception
that data discrimination results include comparative
measures.
3) Association analysis: Association analysis is the
discovery of what are commonly called association
rules. It studies the frequency of items occurring
together in transactional databases, and based on a
threshold called support, identifies the frequent item
sets. Another threshold, confidence, which is the
conditional probability than an item appears in a
transaction when another item appears, is used to
pinpoint association rules. Association analysis is
commonly used for market basket analysis.
Association analysis is for studying the frequency of
attributes or items occurring together during
transaction analysis with a conditional probability
called confidence. This is basically for the discovery
of ‘Association rules’. In agriculture it can be used
for two products being marketed or demanded in
association or credit intake of the farmer occurring
in association with cash crop cultivation and so on.
Conclusions
There is a growing number of applications of data mining
techniques in agriculture and a growing amount of data
that are currently available from many resources. This is
relatively a novel research field and it is expected to grow
in the future. there is a lot of work to be done on this
emerging and interesting research field. The
multidisciplinary approach of integrating computer science
with agriculture will help in forecasting/ managing purpose.
Figure 6 : Examples of Time –Series data
Case Number
153
145
137
129
121
113
105
97
89
81
73
65
57
49
41
33
25
17
9
1
Value
10000
8000
6000
4000
2000
0
Price/RS.
Fit for V2 from ARIM
A, MOD_27 CON

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References
[1]. Abdullah, A., Brobst, S., M.Umer M. (2004). "The case
for an agri data ware house: Enabling analytical
exploration of integrated agricultural data". Proc. of
IASTED International Conference on Databases and
Applications. Austria.
[2]. Chau, m., Cheng, R., and Kao, b.92005). Uncertain
data mining; A new research Direction, in Proceeding
of the Workshop on the Science of the Artificial,
Hualian, Taiwan.
[3]. Codd, E.F. (1993). Providing OLAP (On line Analytical
Processing) to user analysts: An IT mandate.
Technical Report, E.F Codd and Associates.
[4]. Cunningham S.J., G. Holmes. (2005). "Developing
innovative applications in agriculture using data
mining". Proc. Of 3rd International Symposium on
Intelligent Information Technology in Agriculture.
Beijing, China.
[5]. Inmon, B (2005). Building the data Warehouse Fourth
edition, john Wiley, New York.
[6]. Kiran Mai, C., Murali Krishna, I.V., A.Venugopal Reddy
( 2006). "Data Mining of Geo-spatial Database For
Agriculture Related Application". Proc. of Map India.
New Delhi.
[7]. Osmar R.Zaiane (1999). CMPUT690 principles of
Knowledge discovery in Data bases, department of
computer science, University of Alberta.
[8]. Xu, S. and Zhang, W. (2004). PBKM: A secure
Knowledge management Framework. NSF/NSA/AFRL
Workshop on Secure Knowledge Management’04.
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